Updates

Model and report changes

  1. The definition of deaths has been adapted to include all deaths that occur in individuals who have had lab-confirmed infection within 60 days from the date of their most recent positive test. This definition reflects more realistically the burden of COVID-19.
  2. Using observations of improved survival in hospitalised COVID-19 patients, we have allowed the probability of dying following infection with SARS-CoV2 (the infection-fatality rate, IFR) to gradually change over the course of June 2020, with a decrease being estimated.
  3. The model uses seroprevalence data on the presence of COVID-19 antibodies in blood samples taken by NHSBT to estimate the levels of cumulative infection within the population over time. As, from early June, the NHSBT has been giving a constantly declining prevalence of antibodies, these data have been curtailed at this point.

Updated findings

  1. Our current estimate of the number of infections occurring each day across England is 53,200 (35,100–82,100, 95% credible interval).
  2. We predict that the number of deaths each day is likely to be between 230 and 515 on the 31st of October.
  3. We estimate \(R_t\) to be above 1 in most regions with a 100% probability, apart from the Midlands, the South East and London for which the probability of \(R_t\) exceeding 1 is 99%, 97% and 95%, respectively.
  4. London, followed by the North West, continues to have the highest attack rate, that is the proportion of the population who have ever been infected, at 20% and 16% respectively. The South West continues to have the lowest attack rate (4%).

Interpretation

  1. The estimated growth rate for England is 0.07 (0.05–0.08, 95% credible interval) per day. This means that the number of infections is growing by 7% each day. This translates into a doubling in number of new infections approximately every 10-days.
  2. The number of daily infections continue to rise, and are particularly high in the North West and the North East and Yorkshire (18,200 and 16,700 infections per day, respectively). Note that a substantial proportion of these daily infections will be asymptomatic.
  3. Transmission as measured by the Rt values is increasing in the East of England and the South West, while showing a plateauing in the North East and the South East and a slight decrease in the remaining regions.
  4. However, deaths data used are only weakly informative on \(R_t\) over the last two weeks and are still occurring in relatively small numbers. Therefore, the estimate for current incidence, \(R_t\) and the forecast of daily numbers of deaths are very uncertain.

Summary

Real-time tracking of an epidemic, as data accumulate over time, is an essential component of a public health response to a new outbreak. A team of statistical modellers at the MRC Biostatistics Unit (BSU), University of Cambridge, are working to provide regular now-casts and forecasts of COVID-19 infections and deaths. This information feeds directly to the SAGE sub-group, Scientific Pandemic Influenza sub-group on Modelling (SPI-M), and to regional Public Health England (PHE) teams.

Methods

We fit a transmission model (Birrell et al. 2020) to a number of data sources (see ‘Data Sources’), to reconstruct the number of new COVID-19 infections over time in different age groups and NHS regions, estimate a measure of ongoing transmission and predict the number of new COVID-19 deaths.

Data sources

We use:

  1. Data on COVID-19 confirmed deaths from the Public Health England (PHE) line-listing This consists of a combination of deaths notified to:
    • the Demographics Batch Service (DBS), a mechanism that allows PHE to submit a file of patient information to the National Health Service spine for tracing against the personal demographics service (PDS). PHE submit a line list of patients diagnosed with COVID-19 to DBS daily. The file is returned with a death flag and date of death updated (started 20th March, 2020).
    • NHS England, who report data from NHS trusts relating to patients who have died after admission to hospital or within emergency department settings.
    • Health Protection Teams (HPTs), resulting from a select survey created by PHE to capture deaths occurring outside of hospital settings, e.g. care homes (started 23rd March, 2020)
  2. Data on antibody prevalence in blood samples from a PHE survey of NHS Blood Transfusion (NHSBT) donors.

Data are stratified into eight age groups: <1, 1-4, 5-14, 15-24, 25-44, 45-64, 65-74, 75+, and the NHS England regions (North East and Yorkshire, North West, Midlands, East of England, London, South East, South West).

  1. Published information on the the natural history of COVID-19 (Verity et al., 2020; Li et al, 2020)
  2. Information on contacts between different age groups from:
    • A Survey that describes relative rates of contacts between different age groups (Mossong et al. 2008).
    • Google Community Mobility reports, informing the changes in people’s mobility over the course of the pandemic, particularly after the March 23rd lockdown measures.
    • The ONS’ time use survey, which in conjunction with the google mobility study, allows estimation of the changing exposure to infection risk over time.
    • Data from the Department for Education describing the proportion of children currently attending school.

Epidemic summary

Current \(R_t\)

Value of \(R_t\), the average number of secondary infections due to a typical infection today.

Number of infections

Attack rate

The percentage of a given group that has been infected.

By region

By age

IFR

Change in infections incidence

Growth rates

NB: negative growth rates are rates of decline. Values are daily changes.

Region Median 95% CrI (lower) 95% CrI (upper)
England 0.07 0.05 0.08
East of England 0.09 0.04 0.13
London 0.04 -0.01 0.08
Midlands 0.04 0.00 0.07
North East and Yorkshire 0.07 0.05 0.10
North West 0.05 0.03 0.08
South East 0.05 0.00 0.10
South West 0.07 0.01 0.13

Halving times

Halving times in days, if a region shows growth than value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England NA NA NA
East of England NA NA NA
London NA 72.24 NA
Midlands NA NA NA
North East and Yorkshire NA NA NA
North West NA NA NA
South East NA 475.23 NA
South West NA NA NA

Doubling times

Doubling times in days, if a region shows decline then the value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England 10.65 8.62 13.66
East of England 8.04 5.37 18.20
London 17.46 8.61 NA
Midlands 16.62 9.37 313.99
North East and Yorkshire 9.41 7.11 14.77
North West 13.07 9.16 24.37
South East 13.11 6.90 NA
South West 9.41 5.21 64.44

Change in deaths incidence

Growth rates

NB: negative growth rates are rates of decline. Values are daily changes.

Region Median 95% CrI (lower) 95% CrI (upper)
England 0.06 0.05 0.08
East of England 0.08 0.03 0.13
London 0.04 0.00 0.08
Midlands 0.04 0.00 0.07
North East and Yorkshire 0.08 0.05 0.10
North West 0.06 0.03 0.08
South East 0.05 0.00 0.09
South West 0.07 0.01 0.13

Halving times

Halving times in days, if a region shows growth than value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England NA NA NA
East of England NA NA NA
London NA 187.75 NA
Midlands NA NA NA
North East and Yorkshire NA NA NA
North West NA NA NA
South East NA 260.01 NA
South West NA NA NA

Doubling times

Doubling times in days, if a region shows decline then the value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England 10.77 8.70 14.04
East of England 8.46 5.42 20.49
London 17.79 8.60 NA
Midlands 17.90 9.72 175.49
North East and Yorkshire 9.20 6.77 14.70
North West 11.67 8.19 20.93
South East 14.92 7.32 NA
South West 10.35 5.37 101.36

Infections and deaths

The blue lines is show when interventions have been introduced (lockdown on 23 Mar and the relaxation of measures on 11 May), and the red line shows the date these results were produced (17 Oct).

Infection incidence

By region

By age

Cumulative infections

By region

By age

Deaths incidence

By region

By age

Cumulative deaths

By region

By age

Prob \(R_t > 1\)

The figure below shows the probability that \(R_t\) is greater than 1 (ie: the number of infections is growing) in each region over time. Clicking the regions in the legend allows lines to be added or removed from the figure.

\(R_t\)

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